Image retrieving method and apparatuses therefor

ABSTRACT

By sequentially inputting images for each frame, sequentially extracting features from the inputted frame images, converting the features sequentially extracted into a feature series corresponding to the inputted frame image series, compressing the feature series in the direction of the time axis, storing the compressed feature series in the storage, sequentially extracting features separately from the images to be retrieved for each inputted frame, sequentially comparing the features of the images to be retrieved for each frame with the stored compressed feature series, storing the progress state of this comparison, updating the stored progress state of the comparison on the basis of a comparison result with the frame features of the succeeding images to be retrieved, and retrieving image scenes matching with the updated progress state from the images to be retrieved on the basis of the comparison result between the updated progress state and the features of the images to be retrieved for each frame, the present invention can retrieve video images on the air or video images in the data base at high speed and enables self organization of video to be classified and arranged on the basis of the identity of partial images of video.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a retrieving method and apparatusestherefor for video images on the air or video images in a data base orothers and more particularly to a video image retrieving method andapparatuses therefor for performing high-speed retrieval by the help offeatures of video images.

2. Description of the Prior Art

Recently, multi-media information processing systems can store andindicate various types of information such as video and text to users.However, with respect to retrieval of them, a retrieving method using alanguage such as a keyword is mainly used. In this case, a keywordassigning operation is necessary and it is extremely expensive to assigna keyword to each frame of video having a large amount of information.Furthermore, since a keyword is freely assigned by a data baseconstructor, there is a problem imposed that when the viewpoint of auser is different from that of the data base constructor, the keywordwill be useless. In these circumstances, a request for retrieval from aunique image feature in addition to the keyword is made. However, toretrieve information on the basis of the feature of an image, ahigh-speed comparison art between the video feature comprising enormousframes and the feature for the queried image is necessary. As ahigh-speed comparison art only applicable to video images, “Videoretrieving method and apparatuses therefor” is proposed in JapanesePatent Application Laid-Open 7-114567. This method does not compare allthe frames but compares only an image at the time of changing of cut ofimages so as to reduce the processing amount. By doing this, the highspeed also suited to comparison of images on the air is realized. On theother hand, there is a problem imposed that a scene comprising only onecut or a scene in which the cut change timing varies with editing beforeor after cannot be compared satisfactorily. Furthermore, duringretrieval, scenes other than the scene specified as a retrieval key arenot searched in the same way as with other general data base systems, sothat whenever scene retrieval becomes necessary, it is necessary torepeatedly compare a very large amount of video information from thebeginning thereof to the last. The scene comparison process includes anumber of processes such as processes to be performed commonly even ifthe scene to be retrieved is different as well as the feature extractionand reading processes and repetitive execution of such a process is ofno use.

SUMMARY OF THE INVENTION

An object of the present invention is to solve the aforementionedproblems and to provide an image retrieving method for comparing thefeature of a target image to be retrieved and the feature of a sampleimage to be prepared for query at high speed without performing akeyword assigning operation for image retrieval and for detecting thesame segment with the frame accuracy. A target image on the air or inthe data base is applicable.

Another object of the present invention is to provide a method fordetecting the same scene existing in the target image regardless ofwhether it is specified as a retrieval key beforehand in the same way atthe same time with input of the target image.

Still another object of the present invention is to provide a videocamera for comparing, when recording an image series inputted frommoment to moment during picking up of images, those images with recordedimages and recording them in association with matched images.

To accomplish the above objects, the present invention is a signalseries retrieving method and apparatuses therefor in an informationprocessing system comprising a time sequential signal input means, atime sequential signal process controller, and a storage, wherein themethod and apparatuses sequentially input time sequential signals,sequentially extract features in each predetermined period of theinputted time sequential signals, convert the features sequentiallyextracted into a feature series corresponding to the inputtedpredetermined period series, compress the feature series in thedirection of the time axis, store the compressed feature series in thestorage, sequentially extract features from the time sequential signalsto be retrieved in each predetermined period of the inputted timesequential signals, sequentially compare the features of the timesequential signals to be retrieved in each predetermined period with thestored compressed feature series, store the progress state of thecomparison, and retrieve a signal series matching with the progressstate from the time sequential signals to be retrieved on the basis ofthe comparison result between the stored progress state of thecomparison and the features of the time sequential signals to beretrieved in each predetermined period.

More concretely, the present invention divides a video image to becompared into the segment-wise so that the feature of each frame is setin the variation width within the specific range respectively, extractsone or a plurality of features in each segment, stores it or them incorrespondence with the address information indicating the position inthe image in the segment, then sequentially inputs frame images one byone from video images to be retrieved, and when the feature series at anoptional point of time in which the features of the frame images aresequentially arranged and the feature series in which the features inthe segments constituting the stored images are sequentially arranged ineach segment length have portions equal to or more than the specificlength which can be decided to be mutually equivalent to each other,detects the portions as a same image. In this case, when they areequivalent to each other from the top of a segment, the presentinvention obtains the address information corresponding to the segmentand when they are decided to be equivalent to each other from halfway ofa segment, the present invention obtains the relative position from thetop of the segment, and outputs a corrected value of the addressinformation corresponding to the segment as a retrieval result.Furthermore, the present invention collects a frame image seriesinputted as a retrieval target in each segment so that the features ofthe frames are set in the variation width within the specific range,extracts one or a plurality of features in each segment, also stores theinformation corresponding to the address information indicating theposition in the target image in the segment, and adds it to the targetimages to be compared next. Furthermore, with respect to the inputtedfeature series, when there are a plurality of video portions which aredetected to be the same, the present invention groups them, associatesthem to each other, and stores them.

An apparatus realizing the aforementioned retrieving method comprises ameans for dividing an optional image into the segment-wise so that thefeature of each frame is set in the variation width within the specificrange respectively, a means for extracting one or a plurality offeatures in each segment, a means for storing it or them incorrespondence with the address information indicating the position inthe image in the segment, a means for sequentially inputting frameimages one by one from images to be retrieved, a means for retaining thefeature series at an optional point of time in which the features of theframe images are sequentially arranged, a means for generating thefeature series in which the features in the segments constituting thestored images are sequentially arranged in each segment length, and ameans for deciding whether the feature series have portions equal to ormore than the specific length which can be decided to be mutuallyequivalent to each other. The present invention also has a means forobtaining, when they are decided to be equivalent to each other from thetop of a segment, the address information corresponding to the segment,when they are decided to be equivalent to each other from halfway of asegment, obtaining the relative position from the top of the segment,and outputting a corrected value of the address informationcorresponding to the segment as a retrieval result. Furthermore, thepresent invention has a means for collecting a frame image seriesinputted as a retrieval target in each segment so that the features ofthe frames are set in the variation width within the specific range, ameans for extracting one or a plurality of features in each segment, anda means for also storing the information corresponding to the addressinformation indicating the position in the target image in the segmentand adding it to the target images to be compared next. Furthermore,with respect to the inputted feature series, when there are a pluralityof scenes which are detected to be the same, the present invention has ameans for grouping them, associating them to each other, and storingthem.

The foregoing and other objects, advantages, manner of operation andnovel features of the present invention will be understood from thefollowing detailed description when read in connection with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system for executing an embodiment of thepresent invention.

FIG. 2 is a block diagram of a process for executing an embodiment ofthe present invention.

FIG. 3 is a schematic view showing the feature extracting method of anembodiment of the present invention.

FIG. 4 is a schematic view showing the feature comparing method of anembodiment of the present invention.

FIG. 5 is a drawing showing an example of feature comparison flow of anembodiment of the present invention.

FIG. 6 is a schematic view showing an example of the conventionalcomparing method.

FIG. 7 is a schematic view for explaining the comparing method of anembodiment of the present invention.

FIG. 8 is a schematic view for explaining the comparing method of anembodiment of the present invention.

FIG. 9 is a block diagram of a process for executing an embodiment ofthe present invention.

FIGS. 10A and 10B are flow charts of an embodiment of the presentinvention.

FIG. 11 is a drawing showing the feature table structure used in anembodiment of the present invention.

FIG. 12 is a drawing showing the candidate list structure used in anembodiment of the present invention.

FIG. 13 is a drawing showing the candidate structure used in anembodiment of the present invention.

FIG. 14 is a drawing showing the retrieval result table and retrievalsegment structure used in an embodiment of the present invention.

FIG. 15 is a schematic view of a video recorder system applying anembodiment of the present invention.

FIG. 16 is a drawing showing a display screen example during imageretrieval of self organization of video by the present invention.

FIG. 17 is a drawing showing a display screen example during imageretrieval of self organization of video by the present invention.

FIG. 18 is a drawing showing a display screen example during imageretrieval of self organization of video by the present invention.

FIG. 19 is a schematic block diagram when the present invention isapplied to a video camera.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

An embodiment of the present invention will be explained hereunder byreferring to the drawings.

FIG. 1 is an example of a schematic block diagram of the systemconfiguration for realizing the present invention.

Numeral 1 indicates a display such as a CRT, which displays an outputscreen of a computer 2. When the output of the computer is voice, thecomputer 2 outputs it via a speaker 13. An instruction to the computer 2can be issued using a pointing device 3 and a keyboard 4. A videoreproducing apparatus 5 is an optical disk or a video deck. A videosignal outputted from the video reproducing apparatus 5 is sequentiallyconverted to digital image data by a video input device 6 and sent tothe computer. In certain circumstances, an image on the air can befetched and a video signal from a broadcast receiver 7 is inputted tothe video input device 6. When a video server recording an image asdigital data or digital video is used instead of the video reproducingapparatus 5, the video input device 6 is unnecessary or a function forexpanding compressed and recorded image data and converting it toincompressed image data is controlled. If the broadcast is of a digitalsystem, the same may be said with the broadcast receiver 7. Inside thecomputer, digital image data is inputted to a memory 9 via an interface8 and processed by a CPU 10 according to a program stored in the memory9. When video handled by the CPU 10 is sent from the video reproducingapparatus 5, a number (frame No.) is sequentially assigned to each frameimage starting from the top of video. When a frame number is sent to thevideo reproducing apparatus by a control line 11, the apparatus cancontrol so as to reproduce the video of the scene. When video is sentfrom the broadcast receiver 7, no frame number is assigned, so that theapparatus records a sequence number or time starting from a processstart time of 0 as required and uses it instead of the frame number.Various informations can be stored in an external information storage 12as required by the internal process of the computer. Various datacreated by the process which will be explained hereunder is stored inthe memory 9 and referred to as required.

FIG. 2 is a whole block diagram showing the process outline of the imageretrieval process of the present invention. This process is executedinside the computer 2. The process program is stored in the memory 9 andexecuted by the CPU 10. Hereunder, the process will be explained on theassumption that each unit is described as a software procedure to beexecuted by the CPU 10. However, needless to say, a function equivalentto this procedure can be realized by hardware. In the followingexplanation, the processes performed by the software are blocked forconvenience. Therefore, for example, in FIG. 2, the input unit forqueried image indicates an input process for queried image. In thisembodiment, an image of the scene to be found out (hereinafter, called aqueried image) 100 is sequentially inputted for each frame by an inputunit for queried image 102 beforehand prior to retrieval and temporarilystored in the memory 9. A frame feature extractor 106 extracts a feature8 from a frame image 104 in the memory 9. A feature table generator 110pairs up the feature and the top frame number for each segment of astring of features when the feature is within the allowable variationrange, creates a feature table 112, and records it in a storage 114.Also an image 116 to be retrieved is sequentially inputted for eachframe by an input unit for target image to be compared 118 in the sameway as with a queried image and temporarily stored in the memory 9. Aframe feature extractor 122 extracts a feature 124 from a frame image120 in the memory 9. In this case, the frame feature extractor 122performs the exactly same process as that of the frame feature extractor106. A feature comparator 130 compares the newest time sequential arrayof the features 124 sequentially sent from the frame feature extractor122 with a stored feature table 300 (the data content is the same asthat of the feature table 112) for consistency. The progress state ofthe comparison is stored in the storage 126 in the form of a candidateslist 400 which will be described later and updated every input of a newframe. If the features are consistent with each other, the image segmentcorresponding to the feature table is outputted to a storage 128 or theother processor as a retrieved result table 600 which will be describedlater. If any name and attribute are associated with the retrieved imagein this case, it is naturally possible to output the name and attribute.

Next, the process performed by each unit mentioned above will beexplained more in detail.

FIG. 3 shows a series of flow (100 to 114) from input of a queried imageto creation of a feature table. The object of this process is tocompress queried images to a minimum quantity of information which canrepresent the features thereof so as to store more types of queriedimages and compare them in real time at one time. Concretely, featuresare extracted from frame images sequentially inputted first. In thiscase, the feature is explained as information which can be representedby several bytes such as the mean color of the whole frame images. As afeature, in addition to it, patterns generally known such as the shapeof the boundary line and texture of a specific image can be widelyapplied. Furthermore, the time sequential array of obtained features iscollected for each segment within the allowable variation range and onefeature is represented in each segment. A′ or A″ shown in the drawingindicates that assuming A as a standard, the absolute value of thedifference of the feature value of A′ or A″ from that of A is less thana specific threshold value. To each frame of inputted images, framenumbers are sequentially assigned such as t₁, t₂, t₃, - - - , and theframe numbers t_(i), t_(j), t_(k), - - - of the top frame of eachsegment and the features A, B, C, - - - are paired up, and a list isgenerated as a feature table. In this case, video comprises 30 frameimages per second, so that although depending on the kind of an image tobe searched for, assuming that the mean segment length is 10 frames, apermutation pattern comprising 10 or more features can be obtained evenfrom a scene in only several seconds. Furthermore, if the length of eachsegment is added to the restrictions, the number of permutations andcombinations of feature tables becomes extremely large in this case anda performance for sufficiently specifying one scene even in many imagescan be expected.

FIG. 4 schematically shows the situation of comparison (the featurecomparison process 130) between the video image to be retrieved and thequeried image stored beforehand. As mentioned above, with respect totarget images to be retrieved, frame image are sequentially inputted andfeatures are extracted (116 to 124). On the other hand, with the queriedimages compressed in the form of feature table, the features arearranged in the length of each segment and the feature series isreturned from the run-wise to the frame-wide during comparison (130). Inthe case of comparison, a queried image having a feature series matchingwith the feature series in a length more than the specific thresholdvalue which has the newest frame just inputted from the target image asa last end is returned as a retrieved result. In this case, not only acomplete match but also a partial match of the feature series aredetected and when the length of the matched part is more than thethreshold value, it is also returned as a retrieved result. By doingthis, also a scene in which the length is delicately different due toediting can be correctly retrieved.

FIG. 5 shows the comparison process of the present invention more indetail. If, when a feature series in an indefinite length as mentionedabove is compared, the comparison is simply executed, it is necessary torepeat a comparison on the assumption of various frame lengths as shownin FIG. 6 whenever a frame image is newly inputted from the targetimage. The number of inter-frame comparisons in this case is extremelyenormous as shown in the drawing and the comparison process is notsuited especially to comparison in real time such that new frames areinputted one after another at a rate of once per {fraction (1/30)}seconds. The reason is that the comparison process is executed quiteindependently of the previous comparison process every input of a frameand even if a match of a certain degree of length is ascertained by thejust prior process, the information cannot be applied to the nextcomparison process. Therefore, the present invention takes an approachto reduce the comparison process to be performed for one frame input andto stepwise perform the comparison process so as to supplement theprevious process every frame input. Concretely, the comparison isexecuted as indicated below.

(1) When a frame is inputted from the target image, it is searchedwhether there is a feature which is the same as that of the frame in thequeried image and all found frames are temporarily stored as candidates.

(2) When the next frame is inputted from the target image, it is checkedwhether the feature of the frame matches with the feature of the frameimmediately after the frame stored as a candidate immediately before.

(3) When they match with each other, the frame is set as a candidatetogether with the frame stored as a candidate immediately before andwhen they do not match with each other, the frame is excluded from acandidate and a frame having the same feature as that of the justinputted frame is newly added as a candidate. In this case, if the frameexcluded from a candidate is kept consistent for the length (the numberof frames) more than the specific threshold value till that time, thematched segment with the frame set at the top is outputted as aretrieved result.

(4) The aforementioned operations are repeated.

The comparison principle of the present invention will be concretelyexplained hereunder by referring to the example shown in FIG. 5.

Firstly, a new frame is inputted from the target image and the frame (1)in which the feature X is obtained will be considered. Since there isnot the feature X in the queried image, nothing is performed. The samemay be said with the frame (2). When the frame (3) is inputted and thefeature A′ is obtained, there is the feature A matching with A′ in thequeried image, so that all the frames {circle around (1)} to {circlearound (4)} having the feature A in the queried image are set ascandidates. Depending on the appearing condition of features of framesto be inputted hereafter from the target image, any of these candidateframes has a possibility that one segment with the frame set at the topbecomes a scene to be retrieved. In the lower table shown in FIG. 5,{circle around (1)} to {circle around (4)} written on the line of Frame(3) indicate frames in the queried image which are selected ascandidates at this point of time. Also in the next frame (4), thefeature A′ is obtained. Firstly, all the frames selected as candidatesat the preceding step are checked whether the next frames match infeature. As a result, the frames {circle around (1)} to {circle around(3)} match in feature but the frame {circle around (4)} does not matchin feature because the feature of the next frame {circle around (4)} ischanged to B. The portion of x marked on the fourth line in the tableindicates it and the frame {circle around (4)} selected as a candidatein the frame (3) is excluded from a candidate at this point of time. Atthe same time, as candidates in the frame (4), {circle around (1)} to{circle around (4)} which are the same as those of (3) are newly addedon the fourth line in the table. Although the frames {circle around (1)}to {circle around (4)} added on the line (3) are the same as the frames{circle around (1)} to {circle around (4)} added on the line (4), theyare handled as different candidates as comparison candidates.Furthermore, B is obtained in the frame (5) and {circle around (1)} and{circle around (2)} selected as candidates in (3) and {circle around(1)} to {circle around (3)} selected as candidates in (4) are excludedfrom candidates. In the same way, {circle around (5)} and {circle around(6)} are selected as candidates at this point of time. When theaforementioned process is repeated whenever a frame is inputted from thetarget image, candidates matching continuously up to the step of theframe (8) are only {circle around (3)} selected as a candidate in (3),{circle around (4)} selected as a candidate in (4), {circle around (5)}selected as a candidate in (5), {circle around (6)} selected as acandidate in (6), and {circle around (7)} selected as a candidate in(7). At the point of time that the frame (9) is inputted and nocomparison can be made, it is found that the frames (3) to (8) of thetarget image and the queried images {circle around (3)} to {circlearound (8)} have a longest matching segment. These results match withthe comparison results when the comparison of scenes is checked bysequentially changing the length with the frame (8) as starting pointusing the conventional method previously shown in FIG. 6. In the case ofFIG. 6, assuming the number of frames of queried images as n, therepetition time of comparison between the frames to be executed everyone frame input is n(n+1)(n+2)/6 times as shown in FIG. 6 and the orderof the calculated value is 0(n³). However, according to this method,only the sum of (1) the repetition time c of checking for a match of thefeature of a newly inputted frame with the feature of the next frame tothe candidate frame and (2) the repetition time n of checking whetherthere is the same feature as that of the newly inputted frame in thequeried images is acceptable, and generally n>>c, and the order is 0(n).This difference is cased by use of the inductive method for obtainingthe result of adding the current frame on the basis of the processingresult up to the just prior frame. n can be made smaller than theoriginal number of frames by use of the aforementioned feature table anda quicker comparison can be expected. Furthermore, the retrieved resultcan be clearly positioned with the frame accuracy.

In the above explanation, a case of one queried image is assumed.However, the principle can be also applied to a plurality of queriedimages without trouble. For comparison every frame input, it isdesirable only to repeat the aforementioned process for the number ofqueried images. However, as shown in FIG. 7, although the same imagepart is included in each of the queried images, they may be delicatelydifferent in the longitudinal direction due to an effect of a differentediting way. In the drawing, three kinds of ways {circle around (1)},{circle around (2)}, and {circle around (3)} are shown. The same may besaid with a case that a plurality of same image parts are included inone queried image. When only whether there is a matched part in thequeried image is necessary, no problem is imposed. However, depending onthe object of retrieval, also the classification may be required on thebasis of the accurate position and length of the matched segment. Inthis case, it is necessary to clearly output what segment matches withwhat segment as a retrieved result. When there is an overlapped part asshown in No. 2 and No. 3 in the drawing, it is necessary to indicate theoverlapped part in consideration of the inclusion relationship. Themethod of the present invention can process also this problem at highspeed without changing the basic comparison principle. In the comparisonprocess of this method, it is described that when a frame is inputtedfrom the target image and the feature thereof is obtained, a group offrames having the same feature as that of the target image is selectedas candidates from the queried images. In this case, a group of matchedsegments with the frames selected as candidates at the same time set atthe top which reach a length more than the detected threshold value isimages which are equal to each other. In the example shown in FIG. 7,the segment {circle around (2)} exists in each of the three queriedimages and all the top frames of the segments of the queried images areselected as candidates at the same time when the frame corresponding tothe top of the segment {circle around (2)} is inputted from the targetimage. Although there is the possibility that there are other frames tobe selected as candidates at the same time, they are excluded fromcandidates before they reach a length more than the detected thresholdvalue. They reach the end of the segment {circle around (2)} and whenthe next frame is compared, the matched segment in the queried images ofNo.1 and No. 3 is excluded from a candidate. The target image stillcontinues the match with No. 2. However, the segment {circle around (2)}is decided for the present and it is outputted as a retrieved resultthat {circle around (2)} is detected in the queried images No. 1 to No.3. However, even if the segment {circle around (2)} ends, the queriedimage No. 2 continuously remains as a candidate because also the nextframe is still matched with the target image and finally the segment{circle around (3)} is decided. Even if there is a segment on this sideof {circle around (2)} like {circle around (1)}, the matched segment isdetected and decided in the same way. As mentioned above, according tothe method of the present invention, only by performing a brief checkwhen a segment is selected as a candidate or excluded from a candidate,scenes of various variations delicately different in the longitudinaldirection can be discriminated and detected respectively with thecomparison processing amount every frame input kept small.

In the above explanation, a case that queried images are preparedbeforehand and then the target image is retrieved is used. However, thismethod can be applied even if the queried images are just target images.FIG. 8 shows a conceptual diagram thereof. Target images are inputted,and all of them are stored, and they are handled as if they are theaforementioned queried images. It can be realized by the block diagramshown in FIG. 9. Although it is almost similar to the block diagramshown in FIG. 2, the queried images are the same as the target images,so that the process up to extraction of frame features can be shared andthe frame feature 108 is distributed for storage and comparison. By thismechanism, the part of target images inputted past where the newestimage part {circle around (1)} inputted from the target images appearscan be detected at the same time with input. If scenes appear severaltimes past, all of them are detected at the same time on theaforementioned comparison principle, so that they are collected,classified, and arranged for each detected same scene. So to speak, selforganization of video is automatically realized in real time. Forexample, if the present invention is applied to an apparatus forrecording TV programs for several weeks to which a memory capacity forstoring all TV programs for several weeks is installed, the same imageis generally outputted every time at the opening of a program, so thatby detecting the image and collecting the images before and after it,the programs can be arranged in real time at the same time withrecording. If it is found that there are a plurality of same scenes, itis possible to leave only one image and erase the residual images byleaving only pointers, so that the use efficiency of media for recordingcan be improved. Although also a commercial message is one of imagesoutputted repeatedly, to play back a recorded program, the commercialmessage can be automatically skipped as required. In this case, by useof the commercial characteristic that the length is just 15 seconds or30 seconds, the decision performance as to whether it is a commercialmessage is improved.

In the above explanation, the process of realizing the block diagramshown in FIG. 9 can be represented more concretely by the flow chartsshown in FIGS. 10A and 10B. Also the process of realizing the blockdiagram shown in FIG. 2 is self-evident from FIGS. 10A and 10B. In theabove explanation, for simplicity, the feature of the queried image isreturned from the runwise to the frame-wise once and then compared.However, to make the specification closer to the practical use, a methodof comparison in the run-wise state will be indicated hereunder.

Firstly, at Step 200, the apparatus and various variables areinitialized. The variables mc and mm are set to 0. Next, a frame imageis inputted from the target image (Step 202) and the feature F isextracted from the frame image (Step 204). The feature F uses the meanof colors of all pixels existing in the frame image. The color of eachpixel is represented by the three components R, G, and B, and withrespect to the value of each component, the values on the whole screenare averaged respectively, and a set of three values (Ra, Ga, Ba) isobtained, and this set is assumed as the feature F. If a first frame isinputted, a feature table structure 300 shown in FIG. 11 is newlygenerated and F is written into 302 as a feature of the first segment(segment No. 1). In this case, the frame number is also written into 304as a pair. The feature table generated like this will function hereafterfor the already mentioned queried image. In this case, the variable mcindicating the maximum value of the segments stored in the feature tablestructure 300 is incremented by one and the program is returned to Step202 as it is. On the other hand, if the second frame or a subsequentframe is inputted, Step 206 is executed. At Step 206, the feature FC ofthe newest segment (the segment of the segment number mc-1) stored inthe feature table and the current feature F are compared and it isdecided whether the difference is smaller than the threshold value CTH.In this case, although the feature is a set of three values as mentionedabove, only when the differences between the three values are allsmaller than the threshold value CTH, it is represented that thedifference is smaller than the threshold value CTH. If the difference issmaller than the threshold value CTH, it is decided that the framecurrently inputted can be collected in the same segment as that of thejust prior frames and the program goes to Step 208. At Step 208, theloop counter i is reset to 0. i is incremented by 1 every time at Step226 and Steps 210 to 224 are repeated until i becomes larger than mm. Inthis case, mm indicates the number of candidates at the stage ofcontinuous inspection among all images (stored as the feature table 300)inputted until now on the assumption that there is the possibility thatthe part is the same as an image being newly inputted at present. Astructure 500 for storing the status variable indicating the inspectionstage of each of all candidates is generated and managed by a candidatelist structure 400 as shown in FIG. 12. Pointers to the candidatestructure 500 are stored in the candidate list structure 400 anddynamically added or deleted during execution. FIG. 13 shows theconstitution of the candidate structure 500 and the segment number whenit is registered as a candidate is stored as a starting segment numberof comparison 502 and the segment number which starts from the segmentand is a target of comparison at present is stored as a target segmentnumber of comparison 504. A matching frame number counter 506 indicatesthe repetition time of matching since selected as a candidate, that is,the matching segment length. A starting frame offset for comparison 508is a variable necessary for positioning with the frame accuracy byperforming comparison in run-wise, which will be described later.Pointers to starting candidates of simultaneous comparison 510 connect agroup of candidates simultaneously registered to each other in theconnection list format and candidates simultaneously registered can besequentially traced by referring to 510. At Step 210, the program checkswhether the comparison of the candidate i (indicated as a means of thei-th candidate among the mm candidates) is completed to the end of thesegment which is a comparison target at present. When the frame numberobtained by adding the matching frame number counter 506 to the framenumber of the segment indicated by the starting segment number ofcomparison 502 reaches the frame number of the segment next to thesegment which is a comparison target at present, it is found that thecomparison reaches the end. If it does not, the program increments thematching frame number counter of the candidate i by one (Step 216) andgoes to Step 226. If it does, the program refers to the feature of thesegment following the segment which is a comparison target at presentand checks whether the difference between the feature and F is smallerthan the threshold value STH (Step 212). If the difference is smallerthan the threshold value STH, the program changes the segment to becompared to the next segment and continues the comparison (Step 214). Bydoing this, even if the segment changing location is different from theinput image, it can be stably compared. This is a necessary processbecause, since a video signal may be changed due to noise during imageinput and characteristics of the apparatus, the changing point of thesegment is not always the same even if the same image is inputted. Thereason for use of the threshold value STH which is different from thethreshold value CTH deciding the segment change timing is that thechange of an image is absorbed in the same way and a stable comparisonis executed. On the other hand, at Step 212, when the difference islarger than the threshold value STH, the program checks whether thedifference between the feature of the segment which is a comparisontarget at present and the current feature F is smaller than thethreshold value STH (Step 218). If the difference is smaller than thethreshold value STH, the program goes to Step 226 without doinganything. The reason is that since a segment is selected as a candidatenot in frame-wise but in segment-wise and the features do not alwaysmatch with each other starting from the top of the segment, while aninput image having the same feature as that of the segment which is acomparison target at present is obtained, the program only waits bypositioning for the present. If the difference is larger than thethreshold value STH, it is regarded that the features do not match witheach other any more. If the value of the matching frame number counterof the candidate i is larger than the threshold value FTH in this case(Step 220), the program outputs the candidate i as a retrieved scene(Step 222). The program deletes the candidate i from the candidate list(Step 224) and goes to Step 226.

At Step 206, if the difference is larger than the threshold value CTH,it is decided that the currently inputted frame cannot be collected inthe same segment as that of the previous frames and a new segment isadded to the feature table 300 (Step 228). In this case, mc isincremented by one and F is substituted for FC. At Step 230, the loopcounter i is reset to 0. i is incremented by one every time at Step 248and Steps 232 to 246 are repeated until i becomes larger than mm. AtStep 232, the program checks whether the comparison of the candidate iis completed to the end of the segment which is a comparison target atpresent. This can be obtained by the same method as that of Step 210. Ifthe comparison reaches the end, the program changes the segment to becompared to the next segment (Step 234) and if it does not, the programdoes nothing. Next, the program checks whether the difference betweenthe feature of the segment which is a comparison target at present andthe newest feature F is smaller than the threshold value STH (Step 236).If the difference is smaller than the threshold value STH, the programincrements the matching frame number counter of the candidate i by one(Step 238) and goes to Step 248. If the difference is larger than thethreshold value STH, the program checks not only one segment immediatelyafter the segment which is a comparison target at present but also thefollowing segments sequentially and checks whether there is a segmenthaving the same feature as the current feature F (Step 240). If thereis, the program changes the next segment to a segment to be compared,substitutes the difference between the frame number of the segment andthe frame number which is attempted to compare at first for the startingframe offset for comparison 508, and goes to Step 248. Also the framenumbers do not always match with each other starting from the top of thesegment, so that the positioning with the frame accuracy can be executedby use of this offset. In this case, if the size of the offset is largerthan the segment length when it is selected as a candidate, the programgoes to Step 242 by the same handling as that when no matching followingsegment is found. If it is not, it is equivalent to the comparisonstarted from a segment behind the segment selected as a candidate firstand in this case, it is expected that in the comparison started from therear segment, a match is smoothly continued and the processing isduplicated. If, when no matching following segment is found, the valueof the matching frame number counter of the candidate i is larger thanthe threshold value FTH (Step 242), the program outputs the candidate ias a retrieved scene (Step 244). The program deletes the candidate ifrom the candidate list (Step 246) and goes to Step 248. When theprocess for all the candidates ends, the program searches all segmentshaving the same feature as that of the currently inputted frame imagefrom the segments stored in the feature table, generates a candidatestructure having these segments as comparison starting segments, andadds it to the candidate list (Steps 250 to 256).

At Steps 222 and 244 among the aforementioned steps, the program notonly outputs the information of a found scene as it is but also canoutput it in the formats shown in FIG. 14. The retrieved result table600 collects and groups found scenes for each same scene and manages theentry of each group. A group of same scenes is obtained as previouslyexplained in FIG. 7. Each of found scenes is represented by a retrievedsegment structure 700 and the same scenes represent one group in theconnection list format that the scenes have mutually pointers. Pointersto same scenes forming a connection list are stored in 704 and the topframe number of each segment is stored in 702. A pointer to theretrieval segment structure which is the top of the connection listrepresenting a group is stored in 602 as an entry of the group. In thesame group, the segment lengths of all scenes in the group are the same,so that they are paired up with the entry and stored in 604.

When the aforementioned processes are repeated, a scene which appearedonce in the past is detected the moment it appears once again and thetop and length of the segment are positioned with the frame accuracy.The top of the segment is a frame in which the starting frame offset forcomparison of the candidate structure is added to the frame number ofthe segment indicated by the starting segment number of comparison ofthe candidate structure and the length is the value of the matchingframe number counter itself. Hereafter, by collecting each same segment,automatic self organization can be realized. However, in the case of ascene that a still image continues for a long time, a problem alsoarises that by this method reducing the feature of each frame, thecharacteristic time change of the feature cannot be obtained and theprobability of matching with another still image scene by mistakeincreases. If this occurs, needless to say, it can be solved byincreasing the feature for each frame image. Also in the case of a scenethat the feature changes little, even if a shift of several framesoccurs, the features can match with each other. In such a case, aplurality of segments are overlapped and detected in the same range. Asa typical example of it, there is a case that an image just inputtedmatches with a segment a little before in the same cut (one of the unitsconstituting an image, a collected-image segment continuouslyphotographed by a camera). The reason is that the frames in the same cutare well similar to each other on an image basis due to the redundancyof images. If this occurs, by introducing the known detection method forthe cut change timing and performing a process of not regarding as amatch in the same cut, the problem can be avoided.

FIG. 15 is a conceptual diagram showing an embodiment of a nextgeneration video recorder system using the present invention,particularly the method shown in FIG. 8. The system records video of aTV program and also executes the function of the present invention atthe same time. Address information such as a frame number is assigned toeach frame of video to be recorded, and the address information is usedas the frame number 304 of the feature table 300 which is generated bythe present invention, and a one-to-one synchronization is establishedbetween the video data and the feature table. When the recording ends,the feature table and various variables used in the present inventionare stored in a nonvolatile storage so as to be read and restarted whenthe next recording starts. By doing this, it is possible to newly inputimages, compare them with the images already stored in the video archivein real time at the same time, and automatically associate the samescenes with each other. For example, if a program for comparing theinputted images and the theme song portion is already stored, they aresequential programs and can be automatically collected and arranged as asame classification. If, when sequential programs are watched for thefirst time, information is assigned as a common attribute of the wholesequential programs, it is possible to allow an image just inputted toimmediately share the information. As mentioned previously, also acommercial message appearing repeatedly can be detected and skipped.However, only based on a commercial message existing in an imagerecorded and stored, only a limited number of commercial messages can bedetected. Therefore, even when no images are recorded, images arechecked for 24 hours, and a commercial portion is detected from arepetitive scene, and with respect to the images of the commercialportion, although the images are not recorded, only a feature table isgenerated and recorded. By doing this, more commercial messages can bedetected with the image capacity kept unchanged and a commercial messagecan be skipped more securely. As mentioned above, when the presentinvention is mounted in the next generation video recorder system,automatic arrangement of a recorded program and automatic skipping of acommercial message can be simply executed and the usability is extremelyimproved. In the aforementioned embodiment, it is emphasized thatbroadcasting images can be set as an object. However, needless to say,even images stored in a file may be set as an object.

FIG. 16 shows an embodiment of a display screen used for interactionwith a user. A film image of video is played back and displayed on amonitor window 50 on the display of the computer. As a window displayedon the same screen, there are a window 52 for displaying a list oftypical frame images among images, a text window 55 for inputtingattributes of images and scenes, and a window 54 for displayingretrieved results in addition to the window 50. Retrieved results may bedisplayed on the window 52. These windows can be moved to an optionalposition on the screen by operating a cursor 53 which can be freelymoved by the mouse which is one of the pointing device 3. To input text,the keyboard 4 is used. A typical frame displayed on the window 52 is,for example, the top frame of each cut when an image is divided incut-wise. Buttons 51 are buttons for controlling the playback status ofan image and when the buttons are clicked by the mouse, playback, fastfeed, or rewinding of images can be controlled. Scenes to be played backcan be continuously selected by clicking the typical frame imagesdisplayed as a list on the window 52. In this case, as video to beplayed back, images outputted by the video reproducing apparatus 5connected to the computer may be used or digitized images registered inan external information storage may be used. When the video reproducingapparatus 5 is used, the frame number at the top of a scene is sent tothe video reproducing apparatus and the playback is started from thescene corresponding to the frame number. When the playback reaches theframe number at the end of the scene, an instruction for suspending theplayback is sent to the video reproducing apparatus 5. The same may bebasically said with a digitized image, though digital video data is readand then it is converted to drawing data for a computer and displayed asa kind of graphic. When the display process for one frame ends, thedisplay process of the next frame is continuously executed and by doingthis, moving picture images are displayed. In accordance with the timerequired for the display process, the number of frame images to bedisplayed for a fixed time is adjusted so as to prevent images fromrather fast feed or rather slow feed. On the monitor window 50, imagesfrom the broadcast receiver 7 can be also displayed.

The operation procedure for video retrieval by a user using the screenshown in FIG. 16 will be described hereunder. Firstly, he specifies animage to be queried. The simplest method is a method for executing fastfeed or rewinding using the operation buttons 51 and finding an optionalscene by checking images displayed on the monitor window 50. The list oftypical frames arranged on the window 52 is equivalent to the contentsor indexes of a book and by referring to it, he can find a desired scenemore quickly. To specify a scene, there is no need to accurately specifythe range of the scene and it is desirable to specify an optional frameincluded in the scene. In this case, it may be specified by clicking theframe displayed on the monitor window 50 by the mouse. If a frame imageincluded in the image to be queried is displayed in the list of typicalframes on the window 52, it may be clicked by the mouse. Next, on thetext window 55, the user inputs and registers attribute information suchas the selected scene, title of the whole image, and person's name fromthe keyboard. The repetition time of registration is optional and ifthere is no need to reuse the attribute information hereafter, there isno need to register the attribute information at all. Finally, the userpresents a retrieval start request. It can be done by clicking the OKbutton of the text window 55. By doing this, the system starts theretrieval process. The system imaginarily generates a segment with afixed length having the specified frame just in the middle thereof andapplies the segment to the retrieval method of the present invention asan image to be queried. The target image may be newly inputted from thevideo reproducing apparatus. If it is an image which is alreadyregistered as a data base and whose feature table is generated, thecomparison process is performed for the feature table. In this case, ifthe frame specified first is included in the segment of the obtainedretrieved result, it is the retrieved result. Furthermore, it is checkedwhether it is a partial match or a match of the whole segment. In thecase of a match of the whole segment, it is possible to spread thesegment forward and backward and accurately obtain the matched segment.This is a retrieving method utilizing the advantage of the method of thepresent invention which can search for a partially matched segment athigh speed.

Retrieved results are displayed on the window 54. Display contents areattribute information, time information, and others. Or, retrievedresults can be graphically displayed in the format shown in FIG. 17.FIG. 17 is an enlarged view of the window 52 and numeral 800 indicatesan icon image of each typical frame. When a horizontal bar 806 is putunder an icon image, it is found that a retrieved result exists in thescene corresponding to the icon image. When a retrieved result spans aplurality of scenes of an icon image, the bar becomes longer for thepart. The bar is classified by a color or a hatching pattern. For aplurality of scenes found by retrieval of the same scene, the same coloris displayed. On the other hand, for a retrieved result of a scene and aretrieved result of another scene, different colors are displayed. Thelist of typical frames can be used as contents or indexes of images asmentioned above and is very useful for finding an image to be queried.However, a dilemma arises that the typical frames are not all imagesincluded in video and if all images are tabulated, it is difficult tofind a desired image from them. Therefore, it can be considered toextract typical characteristics of scenes indicated by the typicalframes by analyzing video and for example, to find video of a part notincluded in images of the typical frames by displaying each icon image800 together with information 802 representing characteristics and timeinformation 804. Such information representing scene characteristicsincludes existence of a person, camera work (zoom, pan, tilt, etc.),existence of special effect (fade in or out, dissolve, wipe, etc.),existence of title, and others. With respect to the image recognitionmethod for detecting images, Japanese Patent Application Laid-Open7-210409 (applied on Aug. 18, 1995) applied by the inventors of thepresent invention can be used. The related disclosure of Japanese PatentApplication No. 7-210409 is incorporated herein by reference. When themethod of the present invention is applied, it can be useful to dissolvethe dilemma of the list of typical frames by another approach. Withrespect to repetitive scenes, not the whole scenes but some of them maybe included in the list of typical frames. For example, in FIG. 18, whenone of the repetitive scenes is clicked and retrieved by the cursor 53,scenes having the same video part as that of the scene are all found andindicated to the user. The retrieved result is indicated in a form ofemphasizing the icon image of the scene including the retrieved segment,for example, like a star mark 810 superimposed on an icon image 808. Inthis case, if the icon image itself to be displayed is replaced with aframe image in the retrieved segment, the indication is made moreclearly understandable. By doing this, if there is only one image of thesame scene as the scene to be found in the list of typical frames, it ispossible to find a desired scene by the help of it and theserviceableness of the list of typical frames is enhanced. The samemethod can be applied to the video displayed on the monitor window 50and it is also possible to specify a frame displayed by clicking,retrieve the same scenes as the scene including the frame, and jump toone of the found scenes. To realize such a process, a troublesomepreparation such as setting of a link node is conventionally necessary.However, if the method of the present invention is used, very quickretrieval is available, so that it is desirable to execute retrievalwhen necessary and no preparation is necessary.

To execute the self organization process shown in the block diagram inFIG. 9, the user does not need to execute any special process forretrieval and if he just inputs an image, the computer automaticallyexecutes the process.

In the above explanation, the method for retrieving on the basis ofimage characteristics of video is described. However, voicecharacteristics may be used and needless to say, to not only video butalso media which can be successively handled, this retrieval method canbe applied.

FIG. 19 shows an example that the image retrieval art of the presentinvention is applied to a video camera. When power is turned on by apower switch 1961 installed in a process input unit 1960 and picturerecording is instructed by a picture recording button 1962, a voice,image input processor 1910 performs processes of inputting a voicesignal from a microphone 1911 and an image signal from a camera 1912.The process of the voice, image input processor includes the A-Dconversion process and compression process for inputted voice and imagesignals. A feature extraction unit 1970 extracts frame-wise featuresfrom an inputted image signal. The process contents are the same asthose of the frame feature extractor 106 shown in FIGS. 2 and 9. Theextracted features are stored in a memory 1940 as a feature table. Thememory 1940 uses a built-in semiconductor memory and a removable memorycard. Inputted voice and image signals are retained in the memory 1940,read from the memory 1940 by a playback instruction from a playbackbutton 1963, and subjected to the expanding process for signalcompression and the D-A conversion process by the voice, image outputprocessor, and images are outputted to a display screen 1921, and voiceis outputted from a speaker 1922. A controller 1930 manages and controlsthe whole signal process of the video camera. With respect to aninputted image, the feature thereof is extracted for each frame andstored in the memory. The controller 1930 compares the feature of aninputted image with the features of past frames retained in the memory1940. The comparison process may be performed in the same way as withthe feature comparator 130 shown in FIGS. 2 and 9. As a result ofcomparison, the segment of scenes having a similar feature is retainedin the memory 1940 in the same format as that of the retrieved resulttable (128 shown in FIGS. 2 and 9). Numeral 1950 indicates a terminalfor supplying power for driving the video camera and a battery may bemounted. An image retrieval menu button 1964 instructs a brief editingprocess such as rearrangement or deletion of scenes or a process ofinstructing a desired scene and retrieving and playing back similarscenes by pressing the button 1964 several times on the display screen1921 on which a recorded moving picture image is displayed, for example,like FIGS. 16, 17, and 18. With respect to the art for detecting thechanging point of a moving picture image used for sorting of scenes,Japanese Patent Application Laid-Open 7-32027 (applied on Feb. 21, 1995)applied by the inventors of the present invention can be referred to.The related disclosure of Japanese Patent Application No. 7-32027 isincorporated herein by reference. Scenes are retrieved by use of theimage feature comparison process executed in FIGS. 2 and 9. For such avideo camera, it is necessary to adjust the conditions of the featurecomparison process rather loosely. The reason is that unlike a TVprogram, when a user generally picks up images with a video camera, hescarcely picks up exactly same images. Therefore, when similar scenes orpersons in the same style of dress are photographed in a similar size,the comparison condition is set so that they are retrieved as similarscenes. Picked-up images are analyzed at the same time with recordingand grouping for each scene and indexing between similar scenes arecompleted, so that recorded images can be edited immediately afterpicking up and the usability by a user is improved.

Effects of the Invention

According to the present invention, by the aforementioned method,redundant segments with an almost same feature continued are collectedand compared into a unit. Therefore, there is no need to executecomparison for each frame, and the calculation amount can be greatlyreduced, and a form that comparison is falsely executed between thefeature series in frame-wise is taken at the same time, so that themethod is characterized in that the same image segment can be specifiedwith the frame accuracy. Whenever a frame is inputted, only the frame iscompared, so that the processing amount for one frame input is madesmaller and the method is suitable for processing of images requiringthe real time including broadcast images. A plurality of image partsdetected at the same time are exactly same images, so that when they arestored as a set, if a request to search one partial image is presented,the retrieval is completed by indicating another partial image of theset and a very quick response can be expected.

What is claimed is:
 1. A method for describing a video comprising thesteps of: extracting a feature from the video in each of a plurality ofpredetermined periods; comparing extracted features of each of theperiods to obtain a representative feature representing successive onesof the periods in which the extracted features are within an allowablerange, recording the representative feature with length informationindicating the periods that are represented by the representativefeature.
 2. A method for describing a video according to claim 1,wherein the step of extracting includes extracting color as theextracted feature.
 3. A method for describing a video according to claim1, wherein the step of extracting includes extracting a shape of aboundary line as the extracted feature.
 4. A method for describing avideo according to claim 1, wherein the step of extracting includesextracting texture as the extracted feature.
 5. A method for describinga video according to claim 1, wherein the step of extracting includesextracting the feature from at least one frame of the video in each ofthe predetermined periods.
 6. A method for describing a video accordingto claim 1, wherein the step of extracting includes extracting thefeature from each frame of the video as the predetermined period, andthe step of recording the representative feature with length informationincludes recording the length information as a frame number.
 7. A methodfor describing a video comprising the steps of: extracting features fromthe video within specified intervals; setting a first feature as arepresentative feature and an interval of the first feature as a firstsegment, comparing a current one of the extracted features with therepresentative feature, and when a difference is within a threshold,adding the interval of the current extracted feature to the firstsegment, when the difference is lager than the threshold, setting thecurrent extracted feature as another representative feature, and theinterval of the current extracted feature as another segment, recordingeach representative feature with length information indicating thesegment represented by the representative feature.
 8. A method fordescribing a video according to claim 7, wherein the step of extractingincludes extracting color as the extracted feature.
 9. A method fordescribing a video according to claim 7, wherein the step of extractingincludes extracting the feature from at least one frame of the video ineach of the predetermined periods.
 10. A method for describing a videoaccording to claim 7, wherein the step of extracting includes extractingthe feature from each frame of the video as the predetermined period,and the step of recording the representative feature with lengthinformation includes recording the length information as a frame number.11. A program stored on a computer readable storage medium executing avideo describing method on a computer, comprising the instructions of:extracting a feature from the video in each of a plurality ofpredetermined periods; comparing extracted features of each of theperiods to obtain a representative feature representing successive onesof the periods in which the extracted features are within an allowablerange, recording the representative feature with length informationindicating the periods that are represented by the representativefeature.
 12. A program stored on a computer readable storage mediumaccording to claim 11, wherein the step of extracting includesextracting color as the extracted feature.
 13. A program stored on acomputer readable storage medium according to claim 11, wherein the stepof extracting includes extracting a shape of a boundary line as theextracted feature.
 14. A program stored on a computer readable storagemedium according to claim 11, wherein the step of extracting includesextracting texture as the extracted feature.
 15. A program stored on acomputer readable storage medium according to claim 11, wherein the stepof extracting includes extracting the feature from at least one frame ofthe video in each of the predetermined periods.
 16. A program stored ona computer readable storage medium according to claim 11, wherein thestep of extracting includes extracting the feature from each frame ofthe video as the predetermined period, and the step of recording therepresentative feature with length information includes recording thelength information as a frame number.
 17. A program stored on a computerreadable storage medium executing a video describing method on acomputer, comprising instructions of: extracting features from the videowithin specified intervals; setting a first feature as a representativefeature and an interval of the first feature as a first segment,comparing a current one of the extracted features with therepresentative feature, and when a difference is within a threshold,adding the interval of the current extracted feature to the firstsegment, when the difference is larger than the threshold, setting thecurrent extract ed feature as another representative feature, and theinterval of the current extracted feature as another segment, recordingeach representative feature with length information indicating thesegment represented by the representative feature.
 18. A program storedon a computer readable storage medium according to claim 17, wherein thestep of extracting includes extracting color as the extracted feature.19. A program stored on a computer readable storage medium according toclaim 17, wherein the step of extracting includes extracting the featurefrom at least one frame of the video in each of the predeterminedperiods.
 20. A program stored on a computer readable storage mediumaccording to claim 17, wherein the step of extracting includesextracting the feature from each frame of the video as the predeterminedperiod, and the step of recording the representative feature with lengthinformation includes recording the length information as a frame number.21. A computer readable storage medium on which is stored executableinstructions representing a video describing program, wherein executionof the instructions causes a computer to perform the steps of:extracting a feature from the video in each of a plurality ofpredetermined periods; comparing extracted features of each of theperiods to obtain a representative feature representing successive onesof the periods in which the extracted features are within an allowablerange, recording the representative feature with length informationindicating the periods that are represented by the representativefeature.
 22. A computer readable storage medium according to claim 21,wherein the step of extracting includes extracting color as theextracted feature.
 23. A computer readable storage medium according toclaim 21, wherein the step of extracting includes extracting a shape ofa boundary line as the extracted feature.
 24. A computer readablestorage medium according to claim 21, wherein t he step of extractingincludes extracting texture as the extracted feature.
 25. A computerreadable storage medium according to claim 21, wherein the step ofextracting includes extracting the feature from at least one frame ofthe video in each of the predetermined periods.
 26. A computer readablestorage medium according to claim 21, wherein the step of extractingincludes extracting the feature from each frame of the video as thepredetermined period, and the step of recording the representativefeature with length information includes recording the lengthinformation as a frame number.
 27. A computer readable storage medium onwhich is stored executable instructions representing a video describingprogram, wherein execution of the instructions causes a computer toperform the steps of: extracting features from the video with inspecified intervals; setting a first feature as a representative featureand an interval of the first feature as a first segment, comparing acurrent one of the extracted features with the representative feature,and when a difference is within a threshold, adding the interval of thecurrent extracted feature to the first segment, when the difference islager than the threshold, setting the current extracted feature asanother representative feature, and the interval of the currentextracted feature as another segment, recording each representativefeature with length information indicating the segment represented bythe representative feature.
 28. A computer readable storage mediumaccording to claim 27, wherein the step of extracting includesextracting color as the extracted feature.
 29. A computer readablestorage medium according to claim 27, wherein the step of extractingincludes extracting the feature from at least one frame of the video ineach of the predetermined periods.
 30. A computer readable storagemedium according to claim 27, wherein the step of extracting includesextracting the feature from each frame of the video as the predeterminedperiod, and the step of recording the representative feature with lengthinformation includes recording the length information as a frame number.